The Laboratory of Cardiovascular Science has a strong commitment to identifying and studying the process of aging in the myocardium. To identify gene products in heart potentially involved in aging, cDNA microarrays were analyzed with mRNA from left ventricles of Fisher 344 rats. Samples from 12, 18, 24 and 30 months were compared with those from 6 months. Of over 9000 unique cDNAs analyzed as a function of age, 388 (4.3%) showed altered expresson where the balanced differential expression (BDE) was greater than 2.0 (down-regulated) or less than -2.0 (up-regulated). Of the 388 sequences, 125 were unknown ESTs. The number of transcripts with altered abundances generally increased with aging and were as follows: 12 mo. 1 increased (I) and 2 decreased (D); 18 mo. 29 (I), 75 (D); 24 mo. 37 (I), 33 (D); and 30 mo. 182 (I), 67 (D). Of the 388 transcripts with altered abundance, 91% showed changes in expression at one time point, and only 9% showed changes in more than two age groups. Most of the RNAs showing altered expression at multiple time points have been grouped into functional categories. The data indicate that the vast majority of transcripts (greater than 95%) do not change with aging. Only 4.3% of the transcripts showed age-dependent alterations, most of which occurred late in life (64%, 249/388 occurred at 30 months). Of these, 47% (118/249) corresponded to ESTs whose identity remain to be determined. We have now completed an extensive quantitative-PCR analysis to identify gene products whose expression is altered in aging and eliminated numerous false positives. Now that the initial screening has been completed, the aim is to determine the significance of the changes in gene expression with the aging process. For this, molecular analyses are underway to identify which products may actually be involved in the hypothesized signal events responsible for the aging process. As a corollary to this project, we have begun and are near completion of an additional microarray analysis of human control and failing hearts. The data are being compared with that generated in the aging model to identify candidate genes implicated not only in aging, but also in disease.